Recovering the true targets of specific ligands by virtual screening of the protein data bank

Abstract

The Protein Data Bank (PDB) has been processed to extract a screening protein library (sc-PDB) of 2148 entries. A knowledge-based detection algorithm has been applied to 18,000 PDB files to find regular expressions corresponding to either protein, ions, co-factors, solvent, or ligand atoms. The sc-PDB database comprises high-resolution X-ray structures of proteins for which (i) a well-defined active site exists, (ii) the bound-ligand is a small molecular weight molecule. The database has been screened by an inverse docking tool derived from the GOLD program to recover the known target of four unrelated ligands. Both the database and the inverse screening procedures are accurate enough to rank the true target of the four investigated ligands among the top 1% scorers, with 70-100 fold enrichment with respect to random screening. Applying the proposed screening procedure to a small-sized generic ligand was much less accurate suggesting that inverse screening shall be reserved to rather selective compounds.

Pharmacophores in Drug Research

“…The results indicate that these methods can compete well with other approaches based on scalar descriptors or on molecular docking and scoring. [55,56] However, having the advantage that information can easily been traced back from virtual space into molecular structure information, pharmacophore-based modeling enables a successful interaction between a computational chemist and his medicinal chemist counterpart.…”

Structure‐Based Approaches to Target Fishing and Ligand Profiling

“…20 proteins) 6 and 2 true targets were recovered for both ligands, respectively. [29] A couple of years later, Paul et al [30] reported a similar procedure using the Gold docking program [31] but with a set of 2 100 protein-ligand binding sites (sc-PDB) defined at the atomic level from the position of bound ligands. The approach, although based on crude docking scores was able to recover the main targets of four bioactive ligands among the top 1 % scoring entries.…”

A Cheminformatic Toolkit for Mining Biomedical Knowledge

“…Future PubChem links that can be envisioned include The Protein Data Bank (PDB) and manually-curated protein-ligand interaction databases (26,79,(185)(186)(187). Currently, molecular docking and pharmacophore similarity searching algorithms can be used to mine potentially-relevant interactions between small molecule ligands and cellular macromolecules (69,79,83,(87)(88)(89)(90)(91)(92)(93)(94)(186)(187)(188)(189)(190)(191)(192). Thousands of molecular structures including proteins, nucleic acids and macromolecular complexes are already publicly available (193).…”